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- W2937494057 abstract "One of the major trends in research on Intrusion Response Systems is to use a model of the system to be protected and/or a model of the attacker to predict the evolution of the system and of the strategy of the attacker. However, very often, modeled systems exhibit a non-stationary behavior due to changes in their configuration, in the software base and in the users behavior. If not properly captured by the system model, such a non-stationary behavior could lead to divergences between the expected and the actual behaviors, thus invalidating the model-based approach. In this paper, we introduce a model-free technique for self-defense of non-stationary systems based on Q-Learning. We experimentally show that the proposed approach is able to effectively capture the dynamics of the underlying system and quickly adapts to changes in the environment." @default.
- W2937494057 created "2019-04-25" @default.
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- W2937494057 date "2019-02-01" @default.
- W2937494057 modified "2023-09-26" @default.
- W2937494057 title "Towards Self-Defense of Non-Stationary Systems" @default.
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- W2937494057 doi "https://doi.org/10.1109/iccnc.2019.8685487" @default.
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